Regional ecological risk assessment method coupling ecological system health and service

Abstract

The invention relates to the technical field of ecological environment assessment, and discloses a regional ecological risk assessment method coupling ecological system health and service, which is suitable for land utilization policy formulation and risk management of large-scale regions such as drainage basins and ecological sensitive areas.

Description

Technical Field

[0001] This invention relates to the field of ecological environment assessment technology, and more specifically, to a regional ecological risk assessment method that couples ecosystem health and services. Background Technology

[0002] With the development and progress of human society, some land use policies and human activities, such as rapid urbanization and vegetation destruction, have changed land use types, which have had a profound impact on all aspects of the ecosystem. This may lead to a significant increase in ecological risks in many areas, thereby threatening the life safety and stable development within the ecosystem.

[0003] Traditional research frameworks primarily study the impact of one or more specific factors on ecosystems, failing to comprehensively assess ecological risks. Ecosystem services, however, represent the link between ecosystems and human well-being; therefore, an increasing number of scholars are incorporating ecosystem services into ecological risk assessment frameworks. Assessing ecological risks from a human well-being perspective, combining ecological processes and sources of ecological risk, can significantly improve timeliness. Implementing comprehensive assessments focuses not only on impacts on ecosystem services but also on the state of the ecosystem. Some studies indicate that ecosystems should not only provide diverse ecosystem services but also maintain high levels of ecosystem health. Healthy ecosystems are considered the goal of ecological and environmental management, emphasizing ecosystem integrity and providing a foundation for ecosystem assessment. However, current research, primarily ecosystem service-oriented, tends to neglect ecosystem health, potentially leading to findings that overlook the connection between measurement endpoints and ecosystem services, resulting in unclear protective functions and stakeholders in ecological risk assessments. Furthermore, many studies rely on detailed socio-economic statistics for risk assessment, significantly reducing timeliness. Therefore, there is an urgent need to establish a rapid, comprehensive risk assessment model that considers both ecological health and ecosystem services, enabling regional ecological risk assessments to better reflect ecosystem integrity. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in existing technologies, this invention provides a regional ecological risk assessment method that couples ecosystem health and services. Ecosystem services reflect the products of interrelated ecological functions within an ecosystem and can be expressed in the monetized form of ecological function products, i.e., ecosystem service value. After establishing a regional ecosystem risk assessment system, an ecological risk index can be calculated, thereby classifying ecological risks. This method primarily relies on remote sensing imagery and food data, without requiring the collection of large amounts of economic and social data, enabling a convenient and rapid objective assessment of regional risks.

[0005] The above-mentioned technical objective of this invention is achieved through the following technical solution: a regional ecological risk assessment method coupling ecosystem health and services, comprising the following steps:

[0006] S1. Classify land use types, which includes the following steps:

[0007] (1) Using Landsat remote sensing data, after downloading the images, preprocessing was performed, including radiometric calibration, atmospheric correction, image fusion and cropping;

[0008] (2) Based on the preprocessed images, samples were selected for each of the seven land types: cultivated land, forest land, grassland, wetland, water body, construction land and unused land.

[0009] (3) Given the significant differences in hue, texture and other features of different land types in the images, in order to improve the accuracy of sample selection and the distinguishability between classes, different band combination schemes were adopted for sample collection based on the spectral characteristics of various land features.

[0010] (4) Use the random forest classification method to classify the images based on the selected samples;

[0011] (5) During the classification process, the program counts the number of votes for each pixel in each category and determines the category with the highest number of votes as the final category of the pixel, thereby generating the final land use classification map;

[0012] S2. Calculate the ecological health index using the following formula:

[0013]

[0014] Wherein, V represents ecosystem vitality, O is the ecosystem health organization factor, and R represents ecosystem resilience;

[0015] S3. Calculate the value of ecosystem services using the following formula:

[0016]

[0017] Here, ESV stands for Ecosystem Services Value, A i E represents the area of ​​land use type i. ik The equivalent of ecosystem service function k per unit area for land use type i;

[0018] S4. The ecological risk index is characterized by combining the ecological health index and the ecosystem service value, and the risk index is graded and evaluated. The calculation formula for the ecological risk index is as follows:

[0019]

[0020] Among them, ERI stands for Ecological Risk Index;

[0021] S5. Use the global Moran's index to determine the spatial autocorrelation of ERI, and use the local Moran's index to analyze its spatial distribution differences.

[0022] When the global Moran index I > 0, it indicates a spatially clustered state; when I < 0, it indicates a spatially diffuse state; when I = 0, there is no spatial dependency.

[0023] At a significant level, the local Moran index I i When I > 0, the spatial difference between unit i and its neighboring units is small, exhibiting high-high clustering (HH), where high values ​​are within the high-value neighborhood, or low-low clustering (LL), where low values ​​are within the low-value neighborhood; when I i <0 indicates significant ERI differentiation, which suggests potential spatial outliers in the target value, including high-low outliers (HL, high values ​​within low-value communities) and low-high outliers (LH, low values ​​within high-value communities).

[0024] Furthermore, in step S2, the calculation formula for the ecosystem health organization factor is as follows: O = 0.4 × LH + 0.4 × LC + 0.2 × LS = 0.2 × SHDI + 0.2 × SHEI + 0.1 × DIVISION + 0.15 × IJI + 0.15 × CONTAG + 0.2 × PAFRAC (4)

[0026] In the formula, LH represents landscape heterogeneity, LC represents landscape connectivity, LS represents landscape shape, SHEI represents Shannon evenness index, SHDI represents Shannon diversity index, DIVISION represents landscape fragmentation index, IJI represents scatter juxtaposition index, CONTAG represents landscape aggregation index, and PARFAC represents perimeter-area fractal dimension, which is used to represent landscape shape.

[0027] The weights of LH and LC are both set to 0.4, the weight of LS is set to 0.2 and represented by PARFAC, the weights of SHDI and SHEI are both set to 0.2, the weights of IJI and CONTAG are set to 0.15, and the weight of DIVISION is set to 0.1.

[0028] Furthermore, in step S2, the formula for calculating ecosystem resilience is as follows:

[0029] R = 0.6 × ∑P i ·Resili i +0.4×∑P i ·Resist i (5)

[0030] In the formula, Pi represents the area proportion of different land types within the region.

[0031] Furthermore, in step S3, E ik The calculation formula is as follows:

[0032] E ik =e ik ×C1×C2×C3 (6)

[0033] In the formula, e ik The economic value of providing food services per unit area of ​​arable land, C1 is the spatial heterogeneity coefficient; C2 is the social development coefficient; C3 is the population difference coefficient.

[0034] Furthermore, in step S4, the calculated ecological risk index is divided into five categories according to the K-means clustering method: higher risk, high risk, medium risk, low risk, and lower risk.

[0035] Furthermore, in step S5, the formula for calculating the global Moran index is as follows:

[0036]

[0037] Where I is the global Moran exponent; x i and x j represents the observed value of a certain attribute in spatial cell i and its neighboring cell j; n is the number of cells i and j, respectively; x is the average value of the regional variable; W ij It is a spatial weight value, represented by an n-dimensional matrix W(n*n). When region i is adjacent to region j, W... ij =1, otherwise, W ij =0; S 2 It is the mean squared error, and its value ranges from [-1, 1].

[0038] Furthermore, in step S5, the formula for calculating the local Moran index is as follows:

[0039]

[0040] In summary, this invention offers the following advantages: Ecosystem services reflect the products of interrelated ecological functions within an ecosystem and can be expressed through the monetization of these ecological function products, i.e., the value of ecosystem services. After establishing a regional ecosystem risk assessment system, an ecological risk index can be calculated, thereby classifying ecological risks. This method primarily relies on remote sensing imagery and food data, eliminating the need for extensive economic and social data collection, and enabling a convenient and rapid objective assessment of regional risks. Attached Figure Description

[0041] Figure 1 This is a framework diagram of a regional ecological risk assessment method that couples ecosystem health and services in an embodiment of the present invention;

[0042] Figure 2 This refers to the land use type classification results of a certain area in 2000, 2010, and 2020 based on remote sensing data in this embodiment of the invention.

[0043] Figure 3 These are the calculation results of the ecosystem health index of a certain region in 2000, 2010, and 2020 in this embodiment of the invention;

[0044] Figure 4 These are the ecosystem service index results for a certain region in 2000, 2010, and 2020, as described in this embodiment of the invention.

[0045] Figure 5 This refers to the ecosystem risk classification results for a certain region in 2000, 2010, and 2020, as described in this embodiment of the invention.

[0046] Figure 6 This is the result of ecosystem risk clustering distribution in a certain region in 2000, 2010, and 2020, as described in this embodiment of the invention. Detailed Implementation

[0047] The following is in conjunction with the appendix Figure 1-6 The present invention will be described in further detail below.

[0048] Example: The Fenhe River Basin (FRB), located in the eastern part of the Loess Plateau, was selected as the study area. The FRB mainly includes important ecosystems such as river systems, waterfront towns, mountains and hills, and plains and basins. With economic and social development and changes in environmental protection policies, the land use patterns and ecosystems of the basin have undergone significant changes.

[0049] Land use types were classified. Single-scene images from the summer lush vegetation season of 2000, 2010, and 2020 using Landsat remote sensing data were selected for classification, requiring cloud cover ≤10% for each scene. Preprocessing including radiometric calibration, atmospheric correction, image fusion, and cropping was performed in ENVI 5.3 software. The random forest classification method was used to classify the images based on the selected samples, such as... Figure 2 As shown.

[0050] The Ecological Health Index (EHI) is calculated based on the land use type, with the following ecological health indicators calculated separately: Figure 3 As shown.

[0051] Calculating Ecosystem Service Value (ESV):

[0052] The data for the parameter calculations came from the Shanxi Provincial Bureau of Statistics and the National Bureau of Statistics of China. The calculated ecosystem service value E per unit area for different land use types was... ik As shown in Table 2, the spatial distribution of the total ecosystem service value is as follows. Figure 4 As shown, there are significant differences, with lower values ​​mainly concentrated in the central and southern parts of the Fen River Basin; while higher ecosystem service values ​​are mainly located on the east and west sides of the Fen River.

[0053] Table 2 Ecosystem service value per unit area of ​​different land types in the Fenhe River Basin (Unit: Yuan / hm²) 2 )

[0054]

[0055] Calculate the ecological risk index:

[0056] like Figure 5 The figure shows the spatial distribution of the ecological risk index in the Fenhe River Basin from 2000 to 2020. The spatial distribution is divided into five levels: low risk, relatively low risk, medium risk, high risk, and relatively high risk. Areas with relatively high ecological risk are mainly concentrated in the central and southern parts of the Fenhe River Basin; areas with relatively low ecological risk are mainly in the northern, eastern, and western parts of the basin. It can be seen that over the past 20 years, the areas with relatively low risk have shown an increasing trend in the northwest and eastern parts of the basin, while the areas with relatively high risk have slightly increased in the central and southern parts.

[0057] Calculate the Moran index:

[0058] The Moran's Index (ERI) for calculating the ecological risk index of the Fenhe River Basin and studying its spatial clustering can reflect the correlation between ecological risk in a region and ecological risk in other areas, aiming to provide insights for environmental management. The global Moran's Index values ​​for 2000, 2010, and 2020 were 0.766, 0.761, and 0.760, respectively. The results indicate a significant spatial positive correlation between the ecological risk index of the Fenhe River Basin from 2000 to 2020, meaning that the ERI of adjacent units is spatially similar. From 2000 to 2020, the Moran's Index showed a downward trend, indicating that the spatial correlation and clustering effect of ecological risk in the Fenhe River Basin weakened over the past 20 years, reflecting a strengthening of spatial fragmentation of the ecological risk index.

[0059] like Figure 6As shown, using the LISA clustering diagram of the ecological risk index to represent local spatial autocorrelation, it can be seen that two main types of clustering fields exist in the three different periods: high-risk clustering fields and low-risk clustering fields. High-high-risk clustering fields are mainly distributed in the central and southern parts of the Fenhe River Basin, accounting for about 29% of the total number of grids, while low-low-risk clustering fields are mainly distributed in the northwest and eastern parts of the basin, accounting for about 30% of the total number of grids. The number of grids in insignificant areas accounts for about 41%. The distribution of high-high-risk clustering fields surrounds the plains of the Fenhe River Basin, which is also the area with the most human activity and rapid urbanization in Shanxi Province, including several major cities in Shanxi Province, such as Taiyuan and Linfen. Low-low-risk clustering fields are concentrated in mountainous and hilly areas with less population activity. Over the past 20 years, the area of ​​high-high-risk clustering has shown a decreasing trend, especially in the southern region of the Fenhe River Basin, while the distribution of low-low-risk clustering fields has remained almost unchanged. The analysis of spatial clustering effects can provide direction for risk control areas in environmental management.

[0060] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A regional ecological risk assessment method that couples ecosystem health and services, characterized in that, Includes the following steps: S1. Classify land use types, which includes the following steps: (1) Using Landsat remote sensing data, after downloading the images, preprocessing was performed, including radiometric calibration, atmospheric correction, image fusion and cropping; (2) Based on the preprocessed images, samples were selected for each of the seven land types: cultivated land, forest land, grassland, wetland, water body, construction land and unused land. (3) Given the significant differences in hue, texture and other features of different land types in the images, in order to improve the accuracy of sample selection and the distinguishability between classes, different band combination schemes were adopted for sample collection based on the spectral characteristics of various land features. (4) Use the random forest classification method to classify the images based on the selected samples; (5) During the classification process, the program counts the number of votes for each pixel in each category and determines the category with the highest number of votes as the final category of the pixel, thereby generating the final land use classification map; S2. Calculate the ecological health index using the following formula: Wherein, V represents ecosystem vitality, O is the ecosystem health organization factor, and R represents ecosystem resilience; S3. Calculate the value of ecosystem services using the following formula: Here, ESV stands for Ecosystem Services Value, A i E represents the area of ​​land use type i. ik The equivalent of ecosystem service function k per unit area for land use type i; S4. The ecological risk index is characterized by combining the ecological health index and the ecosystem service value, and the risk index is graded and evaluated. The calculation formula for the ecological risk index is as follows: Among them, ERI stands for Ecological Risk Index; S5. Use the global Moran's index to determine the spatial autocorrelation of ERI, and use the local Moran's index to analyze its spatial distribution differences. When the global Moran index I > 0, it indicates a spatially clustered state; when I < 0, it indicates a spatially diffuse state; when I = 0, there is no spatial dependency. At a significant level, the local Moran index I i When I > 0, the spatial difference between unit i and its neighboring units is small, exhibiting high-high clustering (HH), where high values ​​are within the high-value neighborhood, or low-low clustering (LL), where low values ​​are within the low-value neighborhood; when I i <0 indicates significant ERI differentiation, which suggests potential spatial outliers in the target value, including high-low outliers (HL, high values ​​within low-value communities) and low-high outliers (LH, low values ​​within high-value communities).

2. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S2, the formula for calculating the ecosystem health organization factor is as follows: O=0.4×LH+0.4×LC+0.2×LS=0.2×SHDI+0.2×SHEI+0.1×DIVISION+0.15×IJI+0.15×CONTAG+0.2×PAFRAC (4) In the formula, LH represents landscape heterogeneity, LC represents landscape connectivity, LS represents landscape shape, SHEI represents Shannon evenness index, SHDI represents Shannon diversity index, DIVISION represents landscape fragmentation index, IJI represents scatter juxtaposition index, CONTAG represents landscape aggregation index, and PARFAC represents perimeter area fractal dimension, which is used to represent landscape shape. The weights of LH and LC are both set to 0.4, the weight of LS is set to 0.2 and represented by PARFAC, the weights of SHDI and SHEI are both set to 0.2, the weights of IJI and CONTAG are set to 0.15, and the weight of DIVISION is set to 0.

1.

3. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S2, the formula for calculating ecosystem resilience is as follows: R=0.6×∑P i ·Resili i +0.4×∑P i ·Resist i (5) In the formula, Pi represents the area proportion of different land types within the region.

4. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S3, E ik The calculation formula is as follows: AND ik =and ik ×C1×C2×C3 (6) In the formula, e ik The economic value of providing food services per unit area of ​​arable land, C1 is the spatial heterogeneity coefficient; C2 is the social development coefficient; C3 is the population difference coefficient.

5. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S4, the calculated ecological risk index is divided into five categories according to the K-means clustering method: higher risk, high risk, medium risk, low risk, and lower risk.

6. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S5, the global Moran index is calculated using the following formula: Where I is the global Moran exponent; x i and x j represents the observed value of a certain attribute in spatial cell i and its neighboring cell j; n is the number of cells i and j, respectively; x is the average value of the regional variable; W ij It is a spatial weight value, represented by an n-dimensional matrix W(n*n). When region i is adjacent to region j, W... ij =1, otherwise, W ij =0; S 2 It is the mean squared error, and its value ranges from [-1, 1].

7. The regional ecological risk assessment method coupling ecosystem health and services according to claim 1, characterized in that, In step S5, the formula for calculating the local Moran index is as follows:

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